Properly assessing a piece of equipment's operational integrity, herein called “operational health”, is a problem that is widespread. The danger of not accurately assessing a piece of equipment's operational health, i.e. how the equipment is functioning in contrast with its ideal level of performance, is that the equipment could suddenly fail without any forewarning. In a case where the equipment plays a critical role in the functioning of a large production facility such as an oilrig, or is a critical component in a communications network, such a failure could lead to a loss of millions of dollars in lost equipment and revenues.
Even if an organization took a cautious stance and decided to perform an excessive amount of preventive maintenance, this too has a downside. Such maintenance leads to excessive labor costs, and may actually increase the chance of equipment failure due to the faulty performance of a preventative maintenance procedure.
While there are current technologies for monitoring the operational health of a piece of equipment, these technologies have several weaknesses. One of the weaknesses is that these methods individually evaluate multiple reports and alarms from a piece of equipment, where each of these reports or alarms is associated with a different aspect of the overall operational health of the equipment. When an individual value from the report or alarm falls outside of a particular range, an operator is alerted. This is a piecemeal approach, in which the equipment is viewed as merely a collection of individual parts. However, an accurate prediction of the operational health of the entire equipment may only come to light when these individual reports and alarms are evaluated together. Each feedback value may not reveal a potential equipment failure that is only uncovered when the overall equipment is evaluated.
U.S. Pat. No. 6,748,341, whose disclosure is incorporated herein by reference, generally involves a method and device for providing an overall machine health prediction. This is accomplished by generating a set of predictive equations using either historical or real-time calibration data from one or more normally operating machines rather than from the current piece of equipment under evaluation. One of these equations is selected, and the operational parameters for a piece of equipment under evaluation are entered into the equation. The calculated value representing the operational health of the equipment under evaluation is compared to a value determined from historical or real-time data from other normally operating machines. The difference between the predicted and actual operation health values is determined. If the difference is statistically significant, an overall probably of machine abnormality is determined.
While the '341 patent addresses the problem of determining the current, overall operational health of a piece of equipment, it still leaves several weaknesses. First, the '341 patent only teaches comparing a current operational health of a piece of equipment with historical or real-time values of other similar pieces of equipment. However, it doesn't teach comparing the operational health of a piece of equipment with its own historical performance. Using the equipment's own historical performance data is significant because each piece of equipment has a unique, acceptable operational health range due to equipment specific factors. Some of these factors include: the age of the equipment, the environment in which it is being used, and the volume of usage the equipment experiences. These and other factors could lead to an acceptable operational range that is unique for that piece of equipment, even in comparison to other similar equipment. Second, the '341 patent only discusses determining the current operational health of the equipment without projecting the equipment's performance into the future. However, it is useful to know not only the current operational health of a piece of equipment, but also to project a forecast of the equipment's performance into the future. Third, the '341 patent does not teach determining which element of the piece of equipment is likely to cause a deviation in the operational health. It is useful to know which element of the equipment is contributing to the deviation in proper operational health in order to effectively focus preventative maintenance. Fourth, the '341 patent discusses taking “raw data for machine variables of interest” as the data used in determining the current operational status of the equipment. However, an effective predictive maintenance tool should not only take into account raw data for machine variables of interest, but also take into account external feedback concerning the equipment's performance, such as reported user-complaints, typical capacity-utilization of the equipment, and diligence in performing established preventive maintenance routines.
An example of a piece of equipment, the knowledge of whose operational health is critical for the organization utilizing the equipment is a Telephone Network Switch (TNS). A TNS is a central part to a telecommunications network which facilitates the routing of a call from the calling party to the called party. It is important to detect a potential failure of a TNS before an organization experiences a downed communications network due to a failed TNS.
While it is clearly important to know the operational health of a TNS, such a determination may be economically infeasible. A typical TNS is programmed to issue dozens and possibly even hundreds of reports and alarms concerning its operational health. It is challenging and labor intensive to monitor each of these feedback values on a continuing basis. Additionally, several other variables that are not included in these reports and alarms also play an important role in predicting the health of the TNS. Collecting and analyzing the TNS reports and alarms as well as the other variables not included in these reports and alarms may involve too much labor and capital resources to make such a monitoring economically worthwhile.